Comprehensive characterization of 536 patient-derived xenograft models prioritizes candidatesfor targeted treatment

TP53 and DNA-PK as potential biomarkers for enhanced efficacy of Olaparib in colorectal cancer

Olaparib is selected based on the presence of BRCA mutations in patient populations; however, further investigation is still required regarding its effect on restoring homologous recombination (HR) through the inactivation of non-homologous end joining (NHEJ). Therefore, identifying regulators of NHEJ could increase the sensitivity of cancer cells to Olaparib by inhibiting DNA damage repair is a major focus of current research. Loss of DNA-dependent protein kinase (DNA-PK), which is a major components of NHEJ, compromises DNA damage repair, and the resulting increase in DNA damage burden may heighten reliance on poly (ADP-ribose) polymerase (PARP)-dependent DNA repair in cancer cells, rendering them more susceptible to PARP inhibitor therapy. However, DNA-PK alone is not sufficient to enhance the effectiveness of Olaparib, so various adjuvant and combination therapies are being explored. We classified colorectal cancer (CRC) cells based on their sensitivity to Olaparib and found that they were categorized according to TP53 status. Here, we examine the role of DNA-PK in the response to Olaparib, emphasizing its relationship with TP53 status. Our findings indicate that the inhibition of DNA-PK enhances sensitivity to Olaparib and induces phosphorylation of p53 exclusively in cells with TP53 wild-type (WT). Furthermore, using CRC patient-derived cells (PDC) and patient-derived xenograft (PDX) model, we show that the sensitivity of Olaparib is determined TP53 and DNA-PK genotypes. These findings highlight TP53 and DNA-PK as potential predictive biomarkers for optimizing PARP inhibitor-based therapy in CRC.

The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

Lung cancer, the leading cause of cancer mortality, exhibits diverse histologic subtypes and genetic complexities. Numerous preclinical mouse models have been developed to study lung cancer, but data from these models are disparate, siloed, and difficult to compare in a centralized fashion. In this study, we established the Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB), an extensive repository of 1,354 samples from 77 transcriptomic datasets covering 974 samples from genetically engineered mouse models (GEMM), 368 samples from carcinogen-induced models, and 12 samples from a spontaneous model. Meticulous curation and collaboration with data depositors produced a robust and comprehensive database, enhancing the fidelity of the genetic landscape it depicts. The LCAMGDB aligned 859 tumors from GEMMs with human lung cancer mutations, enabling comparative analysis and revealing a pressing need to broaden the diversity of genetic aberrations modeled in the GEMMs. To accompany this resource, a web application was developed that offers researchers intuitive tools for in-depth gene expression analysis. With standardized reprocessing of gene expression data, the LCAMGDB serves as a powerful platform for cross-study comparison and lays the groundwork for future research, aiming to bridge the gap between mouse models and human lung cancer for improved translational relevance. Significance: The Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB) provides a comprehensive and accessible resource for the research community to investigate lung cancer biology in mouse models.

Tumor break load quantitates structural variant-associated genomic instability with biological and clinical relevance across cancers

While structural variants (SVs) are a clear sign of genomic instability, they have not been systematically quantified per patient since declining costs have only recently enabled large-scale profiling. Therefore, the biological and clinical impact of high numbers of SVs in patients is unknown. We introduce tumor break load (TBL), defined as the sum of unbalanced SVs, as a measure for SV-associated genomic instability. Using pan-cancer data from TCGA, PCAWG, and CCLE, we show that a high TBL is associated with significant changes in gene expression in 26/31 cancer types that consistently involve upregulation of DNA damage repair and downregulation of immune response pathways. Patients with a high TBL show a higher risk of recurrence and shorter median survival times for 5/15 cancer types. Our data demonstrate that TBL is a biologically and clinically relevant feature of genomic instability that may aid patient prognostication and treatment stratification. For the datasets analyzed in this study, TBL has been made available in cBioPortal.

Combination therapy with Chicoric acid and PD-1/PD-L1 blockade improves the immunotherapy response in patient-derived ovarian cancer xenograft model

PURPOSE

Limited treatment options exist for refractory ovarian cancer (OC) due to its poor response to immune therapies. Therefore, there is an urgent need to develop new effective treatment strategies. Chicoric acid (CA) is reported to have immune-enhancing properties, but its efficacy in cancer treatment is not well understood. We hypothesize that CA might improve the efficacy of PD-1/PD-L1 blockade immunotherapy in refractory OC patients.

METHODS

Patient-derived xenograft (PDX) models were constructed from chemoresistant advanced high-grade serous ovarian cancer patients. These models were treated with CA, aPD-1/aPD-L1 antibodies, or a combination of both. Single-cell RNA sequencing was performed to analyze the cellular composition of the tumor microenvironment (TME), evaluate treatment efficacy, and explore therapeutic mechanisms. Variations in peripheral blood lymphocytes were analyzed via fluorescence-activated cell sorting. Immunohistochemistry confirmed the variations in tumor-infiltrating lymphocytes and tumor cells.

RESULTS

Immunocompetent peripheral blood mononuclear cell (PBMC)-PDX models were successfully constructed using malignant ascites fluid and PBMCs. After treatment, 158,734 cells from 15 samples were categorized into epithelial cells, T lymphocytes, myeloid cells, fibroblasts, and endothelial cells. CA enhanced the antitumor ability of immune cells against OC cells. Notably, CA stimulated the proliferation of CD45 + and CD3 + cells and promoted the migration of CD8 + and CD4 + T cells from peripheral blood to infiltrate the TME. Additionally, CA enhanced the response of OCs to aPD-L1/aPD-1 treatment, strengthened the interaction between tumor and nontumor cells, and identified APP/CD74 as a critical ligand‒receptor pair. CHI3L1 was also found to be a potential marker for predicting immunotherapy efficacy in OC.

CONCLUSION

This study demonstrated that combination therapy with CA and aPD-1/aPD-L1 might be a promising strategy for treating OC effectively.

PDX models for functional precision oncology and discovery science

Precision oncology relies on detailed molecular analysis of how diverse tumours respond to various therapies, with the aim to optimize treatment outcomes for individual patients. Patient-derived xenograft (PDX) models have been key to preclinical validation of precision oncology approaches, enabling the analysis of each tumour's unique genomic landscape and testing therapies that are predicted to be effective based on specific mutations, gene expression patterns or signalling abnormalities. To extend these standard precision oncology approaches, the field has strived to complement the otherwise static and often descriptive measurements with functional assays, termed functional precision oncology (FPO). By utilizing diverse PDX and PDX-derived models, FPO has gained traction as an effective preclinical and clinical tool to more precisely recapitulate patient biology using in vivo and ex vivo functional assays. Here, we explore advances and limitations of PDX and PDX-derived models for precision oncology and FPO. We also examine the future of PDX models for precision oncology in the age of artificial intelligence. Integrating these two disciplines could be the key to fast, accurate and cost-effective treatment prediction, revolutionizing oncology and providing patients with cancer with the most effective, personalized treatments.

Exploring oncology treatment strategies with tyrosine kinase inhibitors through advanced 3D models (Review)

The limitations of two-dimensional (2D) models in cancer research have hindered progress in fully understanding the complexities of drug resistance and therapeutic failures. However, three-dimensional (3D) models provide a more accurate representation of in vivo environments, capturing critical cellular interactions and dynamics that are essential in evaluating the efficacy and toxicity of tyrosine kinase inhibitors (TKIs). These advanced models enable researchers to explore drug resistance mechanisms with greater precision, optimizing treatment strategies and improving the predictive accuracy of clinical outcomes. By leveraging 3D models, it will be possible to deepen the current understanding of TKIs and drive forward innovations in cancer treatment. The present review discusses the limitations of 2D models and the transformative impact of 3D models on oncology research, highlighting their roles in addressing the challenges of 2D systems and advancing TKI studies.

Cancer brain metastasis: molecular mechanisms and therapeutic strategies

Brain metastases (BMs) are the most common intracranial tumors in adults and the major cause of cancer-related morbidity and mortality. The occurrence of BMs varies according to the type of primary tumors with most frequence in lung cancer, melanoma and breast cancer. Among of them, lung cancer has been reported to have a higher risk of BMs than other types of cancers with 40 ~ 50% of such patients will develop BMs during the course of disease. BMs lead to many neurological complications and result in a poor quality of life and short life span. Although the treatment strategies were improved for brain tumors in the past decades, the prognosis of BMs patients is grim. Poorly understanding of the molecular and cellular characteristics of BMs and the complicated interaction with brain microenvironment are the major reasons for the dismal prognosis of BM patients. Recent studies have enhanced understanding of the mechanisms of BMs. The newly identified potential therapeutic targets and the advanced therapeutic strategies have brought light for a better cure of BMs. In this review, we summarized the mechanisms of BMs during the metastatic course, the molecular and cellular landscapes of BMs, and the advances of novel drug delivery systems for overcoming the obstruction of blood-brain barrier (BBB). We further discussed the challenges of the emerging therapeutic strategies, such as synergistic approach of combining targeted therapy with immunotherapy, which will provide vital clues for realizing the precise and personalized medicine for BM patients in the future.

Chondrosarcoma: New Molecular Insights, Challenges in Near-Patient Preclinical Modeling, and Therapeutic Approaches

Chondrosarcoma (CS), the second most common malignant bone tumor after osteosarcoma, accounts for 20-30% of all malignant bone tumors. It mainly affects adults, middle-aged, and elderly people. The CS family includes various entities displaying peculiar biological, genetic, and epigenetic characteristics and clinical behaviors. Conventional CS is the most common subtype. High-grade, dedifferentiated, and mesenchymal CS, as well as unresectable and metastatic CS, exhibit poor prognoses due to their intrinsic resistance to radiotherapy and chemotherapy, underscoring the urgent need for novel therapeutic strategies. CS research is dealing with several challenges. Experimental studies can rely on animal and patient-derived models, but the paucity of representative near-patient preclinical models has hampered predictive drug screening research. This review describes the main clinical and molecular features of CS subtypes, discussing recent data on the genetic alterations and molecular mechanisms involved in CS pathogenesis and progression. The review provides an overview of the current in vitro and in vivo CS models, discusses their advantages and limitations, and highlights the recent efforts in the development of new targeted therapies against CS dependencies, including IDH1/2 mutations, NAD+ dependency, and SIRT1-HIF-2α axis, or exploring DR5 targeting, antiangiogenic therapies, epigenetic drugs, and immunological approaches. All such strategies, in combination with advanced preclinical modeling and personalized multi-omic profiling, hold promise for improving the survival of patients with advanced CS.

Patient-derived xenograft model in cancer: establishment and applications

The patient-derived xenograft (PDX) model is a crucial in vivo model extensively employed in cancer research that has been shown to maintain the genomic characteristics and pathological structure of patients across various subtypes, metastatic, and diverse treatment histories. Various treatment strategies utilized in PDX models can offer valuable insights into the mechanisms of tumor progression, drug resistance, and the development of novel therapies. This review provides a comprehensive overview of the establishment and applications of PDX models. We present an overview of the history and current status of PDX models, elucidate the diverse construction methodologies employed for different tumors, and conduct a comparative analysis to highlight the distinct advantages and limitations of this model in relation to other in vivo models. The applications are elucidated in the domain of comprehending the mechanisms underlying tumor development and cancer therapy, which highlights broad applications in the fields of chemotherapy, targeted therapy, delivery systems, combination therapy, antibody-drug conjugates and radiotherapy. Furthermore, the combination of the PDX model with multiomics and single-cell analyses for cancer research has also been emphasized. The application of the PDX model in clinical treatment and personalized medicine is additionally emphasized.

Technological advances in clinical individualized medication for cancer therapy: from genes to whole organism

Efforts have been made to leverage technology to accurately identify tumor characteristics and predict how each cancer patient may respond to medications. This involves collecting data from various sources such as genomic data, histological information, functional drug profiling, and drug metabolism using techniques like polymerase chain reaction, sanger sequencing, next-generation sequencing, fluorescence in situ hybridization, immunohistochemistry staining, patient-derived tumor xenograft models, patient-derived organoid models, and therapeutic drug monitoring. The utilization of diverse detection technologies in clinical practice has made "individualized treatment" possible, but the desired level of accuracy has not been fully attained yet. Here, we briefly summarize the conventional and state-of-the-art technologies contributing to individualized medication in clinical settings, aiming to explore therapy options enhancing clinical outcomes.

OrgXenomics: an integrated proteomic knowledge base for patient-derived organoid and xenograft

Patient-derived models (PDMs, particularly organoids and xenografts) are irreplaceable tools for precision medicine, from target development to lead identification, then to preclinical evaluation, and finally to clinical decision-making. So far, PDM-based proteomics has emerged to be one of the cutting-edge directions and massive data have been accumulated. However, such PDM-based proteomic data have not been provided by any of the available databases, and proteomics profiles of all proteins in proteomic study are also completely absent from existing databases. Herein, an integrated database named 'OrgXenomics' was thus developed to provide the proteomic data for PDMs, which was unique in (a) explicitly describing the establishment detail for a wide array of models, (b) systematically providing the proteomic profiles (expression/function/interaction) for all proteins in studied proteomic analysis and (c) comprehensively giving the raw data for diverse organoid/xenograft-based proteomic studies of various diseases. Our OrgXenomics was expected to server as one good complement to existing proteomic databases, and had great implication for the practice of precision medicine, which could be accessed at: https://idrblab.org/orgxenomics/.

Lymphovascular Tumoral Emboli in Inflammatory Breast Cancer Result from Haptotaxis-Mediated Encircling Lymphangiogenesis

Inflammatory breast cancer (IBC) is characterized by numerous tumor emboli within lymphatics. In a recent study, we observed tumor embolic budding both in vitro and in vivo within lymphovascular spaces and proposed this to account for the plethora of tumor emboli seen in IBC. These observations did not address, however, how lymphovascular invasion is initiated or the mechanisms involved. In the present study, using the well-characterized patient-derived xenograft (PDX), Mary-X, which exhibited florid lymphovascular invasion (LVI) in athymic mice (LVI) as defined by E-cadherin-positive tumor emboli within lymphatic channels distinguished by podoplanin and LYVE1 membrane and Prox1 nuclear immunoreactivities and spontaneous spheroidgenesis in vitro and human cases of IBC which showed similar LVI, we compared laser-captured microdissected emboli from Mary-X and from the cases of human IBC to non-embolic areas. Mary-X and IBC emboli expressed high levels of E-cadherin and no evidence of epithelial-mesenchymal transition (EMT). Mary-X spheroids expressed high levels of VEGF, especially VEGF-C, and stimulated both vascular and lymphatic endothelial haptotaxis. We then transplanted Mary-X serially into green, cyano, red, and nestin-green fluorescing protein (GFP-, CFP-, RFP-, and nestin-GFP) transgenic reporter mice in various combinations. Multicolor murine imaging studies indicated that reporter-labeled stroma initially encircled clumps of tumor cells and then served as a scaffold that supported nestin-GFP-labeled endothelial haptotaxis resulting in encircling lymphangiogenesis, confirmed by dual LYVE1 immunofluorescence. The present studies demonstrate a possible mechanism of a critical step of the tumor emboli formation of IBC.

Clinical proteomics towards multiomics in cancer

Recent technological advancements in mass spectrometry (MS)-based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS-based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re-examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi-omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets.

Deciphering resistance mechanisms in cancer: final report of MATCH-R study with a focus on molecular drivers and PDX development

BACKGROUND

Understanding the resistance mechanisms of tumor is crucial for advancing cancer therapies. The prospective MATCH-R trial (NCT02517892), led by Gustave Roussy, aimed to characterize resistance mechanisms to cancer treatments through molecular analysis of fresh tumor biopsies. This report presents the genomic data analysis of the MATCH-R study conducted from 2015 to 2022 and focuses on targeted therapies.

METHODS

The study included resistant metastatic patients (pts) who accepted an image-guided tumor biopsy. After evaluation of tumor content (TC) in frozen tissue biopsies, targeted NGS (10 < TC < 30%) or Whole Exome Sequencing and RNA sequencing (TC > 30%) were performed before and/or after the anticancer therapy. Patient-derived xenografts (PDX) were established by implanting tumor fragments into NOD scid gamma mice and amplified up to five passages.

RESULTS

A total of 1,120 biopsies were collected from 857 pts with the most frequent tumor types being lung (38.8%), digestive (16.3%) and prostate (14.1%) cancer. Molecular targetable driver were identified in 30.9% (n = 265/857) of the patients, with EGFR (41.5%), FGFR2/3 (15.5%), ALK (11.7%), BRAF (6.8%), and KRAS (5.7%) being the most common altered genes. Furthermore, 66.0% (n = 175/265) had a biopsy at progression on targeted therapy. Among resistant cases, 41.1% (n = 72/175) had no identified molecular mechanism, 32.0% (n = 56/175) showed on-target resistance, and 25.1% (n = 44/175) exhibited a by-pass resistance mechanism. Molecular profiling of the 44 patients with by-pass resistance identified 51 variants, with KRAS (13.7%), PIK3CA (11.8%), PTEN (11.8%), NF2 (7.8%), AKT1 (5.9%), and NF1 (5.9%) being the most altered genes. Treatment was tailored for 45% of the patients with a resistance mechanism identified leading to an 11 months median extension of clinical benefit. A total of 341 biopsies were implanted in mice, successfully establishing 136 PDX models achieving a 39.9% success rate. PDX models are available for EGFR (n = 31), FGFR2/3 (n = 26), KRAS (n = 18), ALK (n = 16), BRAF (n = 6) and NTRK (n = 2) driven cancers. These models closely recapitulate the biology of the original tumors in term of molecular alterations and pharmacological status, and served as valuable models to validate overcoming treatment strategies.

CONCLUSION

The MATCH-R study highlights the feasibility of on purpose image guided tumor biopsies and PDX establishment to characterize resistance mechanisms and guide personalized therapies to improve outcomes in pre-treated metastatic patients.

Network modeling links kidney developmental programs and the cancer type-specificity of VHL mutations

Elucidating the molecular dependencies behind the cancer-type specificity of driver mutations may reveal new therapeutic opportunities. We hypothesized that developmental programs would impact the transduction of oncogenic signaling activated by a driver mutation and shape its cancer-type specificity. Therefore, we designed a computational analysis framework by combining single-cell gene expression profiles during fetal organ development, latent factor discovery, and information theory-based differential network analysis to systematically identify transcription factors that selectively respond to driver mutations under the influence of organ-specific developmental programs. After applying this approach to VHL mutations, which are highly specific to clear cell renal cell carcinoma (ccRCC), we revealed important regulators downstream of VHL mutations in ccRCC and used their activities to cluster patients with ccRCC into three subtypes. This classification revealed a more significant difference in prognosis than the previous mRNA profile-based method and was validated in an independent cohort. Moreover, we found that EP300, a key epigenetic factor maintaining the regulatory network of the subtype with the worst prognosis, can be targeted by a small inhibitor, suggesting a potential treatment option for a subset of patients with ccRCC. This work demonstrated an intimate relationship between organ development and oncogenesis from the perspective of systems biology, and the method can be generalized to study the influence of other biological processes on cancer driver mutations.

Aneuploidy as a driver of human cancer

Aneuploidy, an abnormal chromosome composition, is a major contributor to cancer development and progression and an important determinant of cancer therapeutic responses and clinical outcomes. Despite being recognized as a hallmark of human cancer, the exact role of aneuploidy as a 'driver' of cancer is still largely unknown. Identifying the specific genetic elements that underlie the recurrence of common aneuploidies remains a major challenge of cancer genetics. In this Review, we discuss recurrent aneuploidies and their function as drivers of tumor development. We then delve into the context-dependent identification and functional characterization of the driver genes underlying driver aneuploidies and examine emerging strategies to uncover these driver genes using cancer genomics data and cancer models. Lastly, we explore opportunities for targeting driver aneuploidies in cancer by leveraging the functional consequences of these common genetic alterations.

XENTURION is a population-level multidimensional resource of xenografts and tumoroids from metastatic colorectal cancer patients

The breadth and depth at which cancer models are interrogated contribute to the successful clinical translation of drug discovery efforts. In colorectal cancer (CRC), model availability is limited by a dearth of large-scale collections of patient-derived xenografts (PDXs) and paired tumoroids from metastatic disease, where experimental therapies are typically tested. Here we introduce XENTURION, an open-science resource offering a platform of 128 PDX models from patients with metastatic CRC, along with matched PDX-derived tumoroids. Multidimensional omics analyses indicate that tumoroids retain extensive molecular fidelity with parental PDXs. A tumoroid-based trial with the anti-EGFR antibody cetuximab reveals variable sensitivities that are consistent with clinical response biomarkers, mirror tumor growth changes in matched PDXs, and recapitulate EGFR genetic deletion outcomes. Inhibition of adaptive signals upregulated by EGFR blockade increases the magnitude of cetuximab response. These findings illustrate the potential of large living biobanks, providing avenues for molecularly informed preclinical research in oncology.

Xenografting Human Musculoskeletal Sarcomas in Mice, Chick Embryo, and Zebrafish: How to Boost Translational Research

Musculoskeletal sarcomas pose major challenges to researchers and clinicians due to their rarity and heterogeneity. Xenografting human cells or tumor fragments in rodents is a mainstay for the generation of cancer models and for the preclinical trial of novel drugs. Lately, though, technical, intrinsic and ethical concerns together with stricter regulations have significantly curbed the employment of murine patient-derived xenografts (mPDX). In alternatives to murine PDXs, researchers have focused on embryonal systems such as chorioallantoic membrane (CAM) and zebrafish embryos. These systems are time- and cost-effective hosts for tumor fragments and near-patient cells. The CAM of the chick embryo represents a unique vascularized environment to host xenografts with high engraftment rates, allowing for ease of visualization and molecular detection of metastatic cells. Thanks to the transparency of the larvae, zebrafish allow for the tracking of tumor development and metastatization, enabling high-throughput drug screening. This review will focus on xenograft models of musculoskeletal sarcomas to highlight the intrinsic and technically distinctive features of the different hosts, and how they can be exploited to elucidate biological mechanisms beneath the different phases of the tumor's natural history and in drug development. Ultimately, the review suggests the combination of different models as an advantageous approach to boost basic and translational research.

DSBSO-Based XL-MS Analysis of Breast Cancer PDX Tissues to Delineate Protein Interaction Network in Clinical Samples

Protein-protein interactions (PPIs) are fundamental to understanding biological systems as protein complexes are the active molecular modules critical for carrying out cellular functions. Dysfunctional PPIs have been associated with various diseases including cancer. Systems-wide PPI analysis not only sheds light on pathological mechanisms, but also represents a paradigm in identifying potential therapeutic targets. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for defining endogenous PPIs of cellular networks. While proteome-wide studies have been performed in cell lysates, intact cells and tissues, applications of XL-MS in clinical samples have not been reported. In this study, we adopted a DSBSO-based in vivo XL-MS platform to map interaction landscapes from two breast cancer patient-derived xenograft (PDX) models. As a result, we have generated a PDX interaction network comprising 2,557 human proteins and identified interactions unique to breast cancer subtypes. Interestingly, most of the observed differences in PPIs correlated well with protein abundance changes determined by TMT-based proteome quantitation. Collectively, this work has demonstrated the feasibility of XL-MS analysis in clinical samples, and established an analytical workflow for tissue cross-linking that can be generalized for mapping PPIs from patient samples in the future to dissect disease-relevant cellular networks.

Genomic profiling of a multi-lineage and multi-passage patient-derived xenograft biobank reflects heterogeneity of ovarian cancer

Ovarian cancer (OC) manifests as a complex disease characterized by inter- and intra-patient heterogeneity. Despite enhanced biological and genetic insights, OC remains a recalcitrant malignancy with minimal survival improvement. Based on multi-site sampling and a multi-lineage patient-derived xenograft (PDX) establishment strategy, we present herein the establishment of a comprehensive PDX biobank from histologically and molecularly heterogeneous OC patients. Comprehensive profiling of matched PDX and patient samples demonstrates that PDXs closely recapitulate parental tumors. By leveraging multi-lineage models, we reveal that the previously reported genomic disparities of PDX could be mainly attributed to intra-patient spatial heterogeneity instead of substantial model-independent genomic evolution. Moreover, DNA damage response pathway inhibitor (DDRi) screening uncovers heterogeneous responses across models. Prolonged iterative drug exposure recapitulates acquired drug resistance in initially sensitive models. Meanwhile, interrogation of induced drug-resistant (IDR) models reveals that suppressed interferon (IFN) response and activated Wnt/β-catenin signaling contribute to acquired DDRi drug resistance.

Patient-Derived-Xenografts in Mice: A Preclinical Platform for Cancer Research

The use of patient-derived xenografts (PDXs) has dramatically improved drug development programs. PDXs (1) reproduce the pathological features and the genomic profile of the parental tumors more precisely than other preclinical models, and (2) more faithfully predict therapy response. However, PDXs have limitations. These include the inability to completely capture tumor heterogeneity and the role of the immune system, the low engraftment efficiency of certain tumor types, and the consequences of the human-host interactions. Recently, the use of novel mouse strains and specialized engraftment techniques has enabled the generation of "humanized" PDXs, partially overcoming such limitations. Importantly, establishing, characterizing, and maintaining PDXs is costly and requires a significant regulatory, administrative, clinical, and laboratory infrastructure. In this review, we will retrace the historical milestones that led to the implementation of PDXs for cancer research, review the most recent innovations in the field, and discuss future avenues to tackle deficiencies that still exist.

CDKN2A copy number alteration in bladder cancer: Integrative analysis in patient-derived xenografts and cancer patients

Bladder cancer (BlCa) is an extensively heterogeneous disease that leads to great variability in tumor evolution scenarios and lifelong patient surveillance, emphasizing the need for modern, minimally invasive precision medicine. Here, we explored the clinical significance of copy number alterations (CNAs) in BlCa. CNA profiling was performed in 15 patient-derived xenografts (PDXs) and validated in The Cancer Genome Atlas BlCa (TCGA-BLCA; n = 408) and Lindgren et al. (n = 143) cohorts. CDKN2A copy number loss was identified as the most frequent CNA in bladder tumors, associated with reduced CDKN2A expression, tumors of a papillary phenotype, and prolonged PDX survival. The study's screening cohort consisted of 243 BlCa patients, and CDKN2A copy number was assessed in genomic DNA and cell-free DNA (cfDNA) from 217 tumors and 189 pre-treatment serum samples, respectively. CDKN2A copy number loss was correlated with superior disease-free and progression-free survival of non-muscle-invasive BlCa (NMIBC) patients. Moreover, a higher CDKN2A index (CDKN2A/LEP ratio) in pre-treatment cfDNA was associated with advanced tumor stage and grade and short-term NMIBC progression to invasive disease, while multivariate models fitted for CDKN2A index in pre-treatment cfDNA offered superior risk stratification of T1/high-grade and EORTC high-risk patients, enhancing prediction of treatment outcome. CDKN2A copy number status could serve as a minimally invasive tool to improve risk stratification and support personalized prognosis in BlCa.

Mixed responses to targeted therapy driven by chromosomal instability through p53 dysfunction and genome doubling

The phenomenon of mixed/heterogenous treatment responses to cancer therapies within an individual patient presents a challenging clinical scenario. Furthermore, the molecular basis of mixed intra-patient tumor responses remains unclear. Here, we show that patients with metastatic lung adenocarcinoma harbouring co-mutations of EGFR and TP53, are more likely to have mixed intra-patient tumor responses to EGFR tyrosine kinase inhibition (TKI), compared to those with an EGFR mutation alone. The combined presence of whole genome doubling (WGD) and TP53 co-mutations leads to increased genome instability and genomic copy number aberrations in genes implicated in EGFR TKI resistance. Using mouse models and an in vitro isogenic p53-mutant model system, we provide evidence that WGD provides diverse routes to drug resistance by increasing the probability of acquiring copy-number gains or losses relative to non-WGD cells. These data provide a molecular basis for mixed tumor responses to targeted therapy, within an individual patient, with implications for therapeutic strategies.

Evaluation of Copanlisib in Combination with Eribulin in Triple-negative Breast Cancer Patient-derived Xenograft Models

The PI3K pathway regulates essential cellular functions and promotes chemotherapy resistance. Activation of PI3K pathway signaling is commonly observed in triple-negative breast cancer (TNBC). However previous studies that combined PI3K pathway inhibitors with taxane regimens have yielded inconsistent results. We therefore set out to examine whether the combination of copanlisib, a clinical grade pan-PI3K inhibitor, and eribulin, an antimitotic chemotherapy approved for taxane-resistant metastatic breast cancer, improves the antitumor effect in TNBC. A panel of eight TNBC patient-derived xenograft (PDX) models was tested for tumor growth response to copanlisib and eribulin, alone or in combination. Treatment-induced signaling changes were examined by reverse phase protein array, immunohistochemistry (IHC) and 18F-fluorodeoxyglucose PET (18F-FDG PET). Compared with each drug alone, the combination of eribulin and copanlisib led to enhanced tumor growth inhibition, which was observed in both eribulin-sensitive and -resistant TNBC PDX models, regardless of PI3K pathway alterations or PTEN status. Copanlisib reduced PI3K signaling and enhanced eribulin-induced mitotic arrest. The combination enhanced induction of apoptosis compared with each drug alone. Interestingly, eribulin upregulated PI3K pathway signaling in PDX tumors, as demonstrated by increased tracer uptake by 18F-FDG PET scan and AKT phosphorylation by IHC. These changes were inhibited by the addition of copanlisib. These data support further clinical development for the combination of copanlisib and eribulin and led to a phase I/II trial of copanlisib and eribulin in patients with metastatic TNBC.

SIGNIFICANCE

In this research, we demonstrated that the pan-PI3K inhibitor copanlisib enhanced the cytotoxicity of eribulin in a panel of TNBC PDX models. The improved tumor growth inhibition was irrespective of PI3K pathway alteration and was corroborated by the enhanced mitotic arrest and apoptotic induction observed in PDX tumors after combination therapy compared with each drug alone. These data provide the preclinical rationale for the clinical testing in TNBC.

Representation of genomic intratumor heterogeneity in multi-region non-small cell lung cancer patient-derived xenograft models

Patient-derived xenograft (PDX) models are widely used in cancer research. To investigate the genomic fidelity of non-small cell lung cancer PDX models, we established 48 PDX models from 22 patients enrolled in the TRACERx study. Multi-region tumor sampling increased successful PDX engraftment and most models were histologically similar to their parent tumor. Whole-exome sequencing enabled comparison of tumors and PDX models and we provide an adapted mouse reference genome for improved removal of NOD scid gamma (NSG) mouse-derived reads from sequencing data. PDX model establishment caused a genomic bottleneck, with models often representing a single tumor subclone. While distinct tumor subclones were represented in independent models from the same tumor, individual PDX models did not fully recapitulate intratumor heterogeneity. On-going genomic evolution in mice contributed modestly to the genomic distance between tumors and PDX models. Our study highlights the importance of considering primary tumor heterogeneity when using PDX models and emphasizes the benefit of comprehensive tumor sampling.

A Lung Cancer Mouse Model Database

Lung cancer, the leading cause of cancer mortality, exhibits diverse histological subtypes and genetic complexities. Numerous preclinical mouse models have been developed to study lung cancer, but data from these models are disparate, siloed, and difficult to compare in a centralized fashion. Here we established the Lung Cancer Mouse Model Database (LCMMDB), an extensive repository of 1,354 samples from 77 transcriptomic datasets covering 974 samples from genetically engineered mouse models (GEMMs), 368 samples from carcinogen-induced models, and 12 samples from a spontaneous model. Meticulous curation and collaboration with data depositors have produced a robust and comprehensive database, enhancing the fidelity of the genetic landscape it depicts. The LCMMDB aligns 859 tumors from GEMMs with human lung cancer mutations, enabling comparative analysis and revealing a pressing need to broaden the diversity of genetic aberrations modeled in GEMMs. Accompanying this resource, we developed a web application that offers researchers intuitive tools for in-depth gene expression analysis. With standardized reprocessing of gene expression data, the LCMMDB serves as a powerful platform for cross-study comparison and lays the groundwork for future research, aiming to bridge the gap between mouse models and human lung cancer for improved translational relevance.

Defining the challenges and opportunities for using patient-derived models in prostate cancer research

BACKGROUND

There are relatively few widely used models of prostate cancer compared to other common malignancies. This impedes translational prostate cancer research because the range of models does not reflect the diversity of disease seen in clinical practice. In response to this challenge, research laboratories around the world have been developing new patient-derived models of prostate cancer, including xenografts, organoids, and tumor explants.

METHODS

In May 2023, we held a workshop at the Monash University Prato Campus for researchers with expertise in establishing and using a variety of patient-derived models of prostate cancer. This review summarizes our collective ideas on how patient-derived models are currently being used, the common challenges, and future opportunities for maximizing their usefulness in prostate cancer research.

RESULTS

An increasing number of patient-derived models for prostate cancer are being developed. Despite their individual limitations and varying success rates, these models are valuable resources for exploring new concepts in prostate cancer biology and for preclinical testing of potential treatments. Here we focus on the need for larger collections of models that represent the changing treatment landscape of prostate cancer, robust readouts for preclinical testing, improved in vitro culture conditions, and integration of the tumor microenvironment. Additional priorities include ensuring model reproducibility, standardization, and replication, and streamlining the exchange of models and data sets among research groups.

CONCLUSIONS

There are several opportunities to maximize the impact of patient-derived models on prostate cancer research. We must develop large, diverse and accessible cohorts of models and more sophisticated methods for emulating the intricacy of patient tumors. In this way, we can use the samples that are generously donated by patients to advance the outcomes of patients in the future.

The Power and Promise of Patient-Derived Xenografts of Human Breast Cancer

In 2016, a group of researchers engaged in the development of patient-derived xenografts (PDXs) of human breast cancer provided a comprehensive review of the state of the field. In that review, they summarized the clinical problem that PDXs might address, the technical approaches to their generation (including a discussion of host animals and transplant conditions tested), and presented transplantation success (take) rates across groups and across transplantation conditions. At the time, there were just over 500 unique PDX models created by these investigators representing all three clinically defined subtypes (ER+, HER2+, and TNBC). Today, many of these PDX resources have at least doubled in size, and several more PDX development groups now exist, such that there may be well upward of 1000 PDX models of human breast cancer in existence worldwide. They also presented a series of open questions for the field. Many of these questions have been addressed. However, several remain open, or only partially addressed. Herein, we revisit these questions, and recount the progress that has been made in a number of areas with respect to generation, characterization, and use of PDXs in translational research, and re-present questions that remain open. These open questions, and others, are now being addressed not only by individual investigators, but also large, well-funded consortia including the PDXNet program of the National Cancer Institute in the United States, and the EuroPDX Consortium, an organization of PDX developers across Europe. Finally, we discuss the new opportunities in PDX-based research.

Combined KRAS-MAPK pathway inhibitors and HER2-directed drug conjugate is efficacious in pancreatic cancer

Targeting the mitogen-activated protein kinase (MAPK) cascade in pancreatic ductal adenocarcinoma (PDAC) remains clinically unsuccessful. We aim to develop a MAPK inhibitor-based therapeutic combination with strong preclinical efficacy. Utilizing a reverse-phase protein array, we observe rapid phospho-activation of human epidermal growth factor receptor 2 (HER2) in PDAC cells upon pharmacological MAPK inhibition. Mechanistically, MAPK inhibitors lead to swift proteasomal degradation of dual-specificity phosphatase 6 (DUSP6). The carboxy terminus of HER2, containing a TEY motif also present in extracellular signal-regulated kinase 1/2 (ERK1/2), facilitates binding with DUSP6, enhancing its phosphatase activity to dephosphorylate HER2. In the presence of MAPK inhibitors, DUSP6 dissociates from the protective effect of the RING E3 ligase tripartite motif containing 21, resulting in its degradation. In PDAC patient-derived xenograft (PDX) models, combining ERK and HER inhibitors slows tumour growth and requires cytotoxic chemotherapy to achieve tumour regression. Alternatively, MAPK inhibitors with trastuzumab deruxtecan, an anti-HER2 antibody conjugated with cytotoxic chemotherapy, lead to sustained tumour regression in most tested PDXs without causing noticeable toxicity. Additionally, KRAS inhibitors also activate HER2, supporting testing the combination of KRAS inhibitors and trastuzumab deruxtecan in PDAC. This study identifies a rational and promising therapeutic combination for clinical testing in PDAC patients.

Integrating Optical Genome Mapping and Whole Genome Sequencing in Somatic Structural Variant Detection

Structural variants drive tumorigenesis by disrupting normal gene function through insertions, inversions, translocations, and copy number changes, including deletions and duplications. Detecting structural variants is crucial for revealing their roles in tumor development, clinical outcomes, and personalized therapy. Presently, most studies rely on short-read data from next-generation sequencing that aligns back to a reference genome to determine if and, if so, where a structural variant occurs. However, structural variant discovery by short-read sequencing is challenging, primarily because of the difficulty in mapping regions of repetitive sequences. Optical genome mapping (OGM) is a recent technology used for imaging and assembling long DNA strands to detect structural variations. To capture the structural variant landscape more thoroughly in the human genome, we developed an integrated pipeline that combines Bionano OGM and Illumina whole-genome sequencing and applied it to samples from 29 pediatric B-ALL patients. The addition of OGM allowed us to identify 511 deletions, 506 insertions, 93 duplications/gains, and 145 translocations that were otherwise missed in the short-read data. Moreover, we identified several novel gene fusions, the expression of which was confirmed by RNA sequencing. Our results highlight the benefit of integrating OGM and short-read detection methods to obtain a comprehensive analysis of genetic variation that can aid in clinical diagnosis, provide new therapeutic targets, and improve personalized medicine in cancers driven by structural variation.

From the identification of actionable molecular targets to the generation of faithful neuroblastoma patient-derived preclinical models

BACKGROUND

Neuroblastoma (NB) represents the most frequent and aggressive form of extracranial solid tumor of infants. Although the overall survival of patients with NB has improved in the last years, more than 50% of high-risk patients still undergo a relapse. Thus, in the era of precision/personalized medicine, the need for high-risk NB patient-specific therapies is urgent.

METHODS

Within the PeRsonalizEd Medicine (PREME) program, patient-derived NB tumors and bone marrow (BM)-infiltrating NB cells, derived from either iliac crests or tumor bone lesions, underwent to histological and to flow cytometry immunophenotyping, respectively. BM samples containing a NB cells infiltration from 1 to 50 percent, underwent to a subsequent NB cells enrichment using immune-magnetic manipulation. Then, NB samples were used for the identification of actionable targets and for the generation of 3D/tumor-spheres and Patient-Derived Xenografts (PDX) and Cell PDX (CPDX) preclinical models.

RESULTS

Eighty-four percent of NB-patients showed potentially therapeutically targetable somatic alterations (including point mutations, copy number variations and mRNA over-expression). Sixty-six percent of samples showed alterations, graded as "very high priority", that are validated to be directly targetable by an approved drug or an investigational agent. A molecular targeted therapy was applied for four patients, while a genetic counseling was suggested to two patients having one pathogenic germline variant in known cancer predisposition genes. Out of eleven samples implanted in mice, five gave rise to (C)PDX, all preserved in a local PDX Bio-bank. Interestingly, comparing all molecular alterations and histological and immunophenotypic features among the original patient's tumors and PDX/CPDX up to second generation, a high grade of similarity was observed. Notably, also 3D models conserved immunophenotypic features and molecular alterations of the original tumors.

CONCLUSIONS

PREME confirms the possibility of identifying targetable genomic alterations in NB, indeed, a molecular targeted therapy was applied to four NB patients. PREME paves the way to the creation of clinically relevant repositories of faithful patient-derived (C)PDX and 3D models, on which testing precision, NB standard-of-care and experimental medicines.

Homologous Recombination Deficiency Unrelated to Platinum and PARP Inhibitor Response in Cell Line Libraries

While large publicly available cancer cell line databases are invaluable for preclinical drug discovery and biomarker development, the association between homologous recombination deficiency (HRD) and drug sensitivity in these resources remains unclear. In this study, we comprehensively analyzed molecular profiles and drug screening data from the Cancer Cell Line Encyclopedia. Unexpectedly, gene alterations in BRCA1/2 or homologous recombination-related genes, HRD scores, or mutational signature 3 were not positively correlated with sensitivity to platinum agents or PARP inhibitors. Rather, higher HRD scores and mutational signature 3 were significantly associated with resistance to these agents in multiple assays. These findings were consistent when analyzing exclusively breast and ovarian cancer cell lines and when using data from the COSMIC Cell Line Project. Collectively, the existing data from established cancer cell lines do not reflect the expected association between HRD status and drug response to platinum agents and PARP inhibitors in clinical tumors. This discrepancy may extend to other tumor characteristics, highlighting the importance of recognizing potential limitations in cell line data for researchers.

Engineering Heterogeneous Tumor Models for Biomedical Applications

Tumor tissue engineering holds great promise for replicating the physiological and behavioral characteristics of tumors in vitro. Advances in this field have led to new opportunities for studying the tumor microenvironment and exploring potential anti-cancer therapeutics. However, the main obstacle to the widespread adoption of tumor models is the poor understanding and insufficient reconstruction of tumor heterogeneity. In this review, the current progress of engineering heterogeneous tumor models is discussed. First, the major components of tumor heterogeneity are summarized, which encompasses various signaling pathways, cell proliferations, and spatial configurations. Then, contemporary approaches are elucidated in tumor engineering that are guided by fundamental principles of tumor biology, and the potential of a bottom-up approach in tumor engineering is highlighted. Additionally, the characterization approaches and biomedical applications of tumor models are discussed, emphasizing the significant role of engineered tumor models in scientific research and clinical trials. Lastly, the challenges of heterogeneous tumor models in promoting oncology research and tumor therapy are described and key directions for future research are provided.

Scrutiny of genome-wide somatic mutation profiles in centenarians identifies the key genomic regions for human longevity

Somatic mutations accumulate with age and are associated closely with human health, their characterization in longevity cohorts remains largely unknown. Here, by analyzing whole genome somatic mutation profiles in 73 centenarians and 51 younger controls in China, we found that centenarian genomes are characterized by a markedly skewed distribution of somatic mutations, with many genomic regions being specifically conserved but displaying a high function potential. This, together with the observed more efficient DNA repair ability in the long-lived individuals, supports the existence of key genomic regions for human survival during aging, with their integrity being of essential to human longevity.

Combining the Tyrosine Kinase Inhibitor Cabozantinib and the mTORC1/2 Inhibitor Sapanisertib Blocks ERK Pathway Activity and Suppresses Tumor Growth in Renal Cell Carcinoma

Current treatment approaches for renal cell carcinoma (RCC) face challenges in achieving durable tumor responses due to tumor heterogeneity and drug resistance. Combination therapies that leverage tumor molecular profiles could offer an avenue for enhancing treatment efficacy and addressing the limitations of current therapies. To identify effective strategies for treating RCC, we selected ten drugs guided by tumor biology to test in six RCC patient-derived xenograft (PDX) models. The multitargeted tyrosine kinase inhibitor (TKI) cabozantinib and mTORC1/2 inhibitor sapanisertib emerged as the most effective drugs, particularly when combined. The combination demonstrated favorable tolerability and inhibited tumor growth or induced tumor regression in all models, including two from patients who experienced treatment failure with FDA-approved TKI and immunotherapy combinations. In cabozantinib-treated samples, imaging analysis revealed a significant reduction in vascular density, and single-nucleus RNA sequencing (snRNA-seq) analysis indicated a decreased proportion of endothelial cells in the tumors. SnRNA-seq data further identified a tumor subpopulation enriched with cell-cycle activity that exhibited heightened sensitivity to the cabozantinib and sapanisertib combination. Conversely, activation of the epithelial-mesenchymal transition pathway, detected at the protein level, was associated with drug resistance in residual tumors following combination treatment. The combination effectively restrained ERK phosphorylation and reduced expression of ERK downstream transcription factors and their target genes implicated in cell-cycle control and apoptosis. This study highlights the potential of the cabozantinib plus sapanisertib combination as a promising treatment approach for patients with RCC, particularly those whose tumors progressed on immune checkpoint inhibitors and other TKIs.

SIGNIFICANCE

The molecular-guided therapeutic strategy of combining cabozantinib and sapanisertib restrains ERK activity to effectively suppress growth of renal cell carcinomas, including those unresponsive to immune checkpoint inhibitors.

Therapeutic target biomarkers of patient-derived xenograft models of gastric-type cervical adenocarcinoma

Background

Most cervical adenocarcinomas are associated with human papillomavirus (HPV). Gastric-type cervical adenocarcinoma (GAS), an HPV-independent adenocarcinoma, shows an aggressive clinical feature, resulting in a poor prognosis. Resistance to chemotherapy poses a difficulty in managing patients with metastatic GAS. We aimed to establish patient-derived xenografts (PDXs) of tumors from two patients with GAS and evaluated protein biomarkers for drug development using immunohistochemistry.

Methods

Two PDXs were established 78 and 48 days after transplanting the patient's tumor tissues into immunodeficient mice, respectively. PDX and patient's tumor samples were stained for HER2, HER3, PMS2, MSH6, PanTrk, and ARID1A to evaluate biomarkers for therapeutic targets. In addition, whole exome sequencing and RNA sequencing were performed on available samples.

Results

The pathological findings in morphological features and immunohistochemical profiles from the established PDXs were similar to those from the patients' surgical tumor specimens. HER3 was overexpressed in the patient's tumors, and the corresponding PDX tumors and HER2 was weakly stained in both types of tumor samples. In all PDX and patient tumor samples, PMS2, MSH6, and ARID1A were retained, and PanTrk was not expressed. In addition, a total of 10 samples, including tumor tissue samples from 8 other GAS patients, were evaluated for HER3 expression scores, all of which were 2 + or higher.

Conclusions

In summary, we evaluated biomarkers for therapeutic targets using newly established PDX models of GAS. Frequent HER3 overexpression and HER2 expression in GAS tumors suggest the possibility of new treatments for patients with GAS by targeting HER3 and HER2.

Comparative RNA-Seq Analysis Revealed Tissue-Specific Splicing Variations during the Generation of the PDX Model

Tissue-specific gene expression generates fundamental differences in the function of each tissue and affects the characteristics of the tumors that are created as a result. However, it is unclear how much the tissue specificity is conserved during grafting of the primary tumor into an immune-compromised mouse model. Here, we performed a comparative RNA-seq analysis of four different primary-patient derived xenograft (PDX) tumors. The analysis revealed a conserved RNA biotype distribution of primary-PDX pairs, as revealed by previous works. Interestingly, we detected significant changes in the splicing pattern of PDX, which was mainly comprised of skipped exons. This was confirmed by splicing variant-specific RT-PCR analysis. On the other hand, the correlation analysis for the tissue-specific genes indicated overall strong positive correlations between the primary and PDX tumor pairs, with the exception of gastric cancer cases, which showed an inverse correlation. These data propose a tissue-specific change in splicing events during PDX formation as a variable factor that affects primary-PDX integrity.

Genomic profiling of subcutaneous patient-derived xenografts reveals immune constraints on tumor evolution in childhood solid cancer

Subcutaneous patient-derived xenografts (PDXs) are an important tool for childhood cancer research. Here, we describe a resource of 68 early passage PDXs established from 65 pediatric solid tumor patients. Through genomic profiling of paired PDXs and patient tumors (PTs), we observe low mutational similarity in about 30% of the PT/PDX pairs. Clonal analysis in these pairs show an aggressive PT minor subclone seeds the major clone in the PDX. We show evidence that this subclone is more immunogenic and is likely suppressed by immune responses in the PT. These results suggest interplay between intratumoral heterogeneity and antitumor immunity may underlie the genetic disparity between PTs and PDXs. We further show that PDXs generally recapitulate PTs in copy number and transcriptomic profiles. Finally, we report a gene fusion LRPAP1-PDGFRA. In summary, we report a childhood cancer PDX resource and our study highlights the role of immune constraints on tumor evolution.

Progress in building clinically relevant patient-derived tumor xenograft models for cancer research

Patient-derived tumor xenograft (PDX) models, a method involving the surgical extraction of tumor tissues from cancer patients and subsequent transplantation into immunodeficient mice, have emerged as a pivotal approach in translational research, particularly in advancing precision medicine. As the first stage of PDX development, the patient-derived orthotopic xenograft (PDOX) models implant tumor tissue in mice in the corresponding anatomical locations of the patient. The PDOX models have several advantages, including high fidelity to the original tumor, heightened drug sensitivity, and an elevated rate of successful transplantation. However, the PDOX models present significant challenges, requiring advanced surgical techniques and resource-intensive imaging technologies, which limit its application. And then, the humanized mouse models, as well as the zebrafish models, were developed. Humanized mouse models contain a human immune environment resembling the tumor and immune system interplay. The humanized mouse models are a hot topic in PDX model research. Regarding zebrafish patient-derived tumor xenografts (zPDX) and patient-derived organoids (PDO) as promising models for studying cancer and drug discovery, zPDX models are used to transplant tumors into zebrafish as novel personalized medical animal models with the advantage of reducing patient waiting time. PDO models provide a cost-effective approach for drug testing that replicates the in vivo environment and preserves important tumor-related information for patients. The present review highlights the functional characteristics of each new phase of PDX and provides insights into the challenges and prospective developments in this rapidly evolving field.

MacroH2A restricts inflammatory gene expression in melanoma cancer-associated fibroblasts by coordinating chromatin looping

MacroH2A has established tumour suppressive functions in melanoma and other cancers, but an unappreciated role in the tumour microenvironment. Using an autochthonous, immunocompetent mouse model of melanoma, we demonstrate that mice devoid of macroH2A variants exhibit increased tumour burden compared with wild-type counterparts. MacroH2A-deficient tumours accumulate immunosuppressive monocytes and are depleted of functional cytotoxic T cells, characteristics consistent with a compromised anti-tumour response. Single cell and spatial transcriptomics identify increased dedifferentiation along the neural crest lineage of the tumour compartment and increased frequency and activation of cancer-associated fibroblasts following macroH2A loss. Mechanistically, macroH2A-deficient cancer-associated fibroblasts display increased myeloid chemoattractant activity as a consequence of hyperinducible expression of inflammatory genes, which is enforced by increased chromatin looping of their promoters to enhancers that gain H3K27ac. In summary, we reveal a tumour suppressive role for macroH2A variants through the regulation of chromatin architecture in the tumour stroma with potential implications for human melanoma.

High-Throughput Drug Screening of Primary Tumor Cells Identifies Therapeutic Strategies for Treating Children with High-Risk Cancer

For one-third of patients with pediatric cancer enrolled in precision medicine programs, molecular profiling does not result in a therapeutic recommendation. To identify potential strategies for treating these high-risk pediatric patients, we performed in vitro screening of 125 patient-derived samples against a library of 126 anticancer drugs. Tumor cell expansion did not influence drug responses, and 82% of the screens on expanded tumor cells were completed while the patients were still under clinical care. High-throughput drug screening (HTS) confirmed known associations between activating genomic alterations in NTRK, BRAF, and ALK and responses to matching targeted drugs. The in vitro results were further validated in patient-derived xenograft models in vivo and were consistent with clinical responses in treated patients. In addition, effective combinations could be predicted by correlating sensitivity profiles between drugs. Furthermore, molecular integration with HTS identified biomarkers of sensitivity to WEE1 and MEK inhibition. Incorporating HTS into precision medicine programs is a powerful tool to accelerate the improved identification of effective biomarker-driven therapeutic strategies for treating high-risk pediatric cancers.

SIGNIFICANCE

Integrating HTS with molecular profiling is a powerful tool for expanding precision medicine to support drug treatment recommendations and broaden the therapeutic options available to high-risk pediatric cancers.

Organoids: approaches and utility in cancer research

ABSTRACT

Organoids are three-dimensional cellular structures with self-organizing and self-differentiation capacities. They faithfully recapitulate structures and functions of in vivo organs as represented by functionality and microstructural definitions. Heterogeneity in in vitro disease modeling is one of the main reasons for anti-cancer therapy failures. Establishing a powerful model to represent tumor heterogeneity is crucial for elucidating tumor biology and developing effective therapeutic strategies. Tumor organoids can retain the original tumor heterogeneity and are commonly used to mimic the cancer microenvironment when co-cultured with fibroblasts and immune cells; therefore, considerable effort has been made recently to promote the use of this new technology from basic research to clinical studies in tumors. In combination with gene editing technology and microfluidic chip systems, engineered tumor organoids show promising abilities to recapitulate tumorigenesis and metastasis. In many studies, the responses of tumor organoids to various drugs have shown a positive correlation with patient responses. Owing to these consistent responses and personalized characteristics with patient data, tumor organoids show excellent potential for preclinical research. Here, we summarize the properties of different tumor models and review their current state and progress in tumor organoids. We further discuss the substantial challenges and prospects in the rapidly developing tumor organoid field.

Targeted therapy for rare lung cancers: Status, challenges, and prospects

Lung cancer causes the most cancer-related deaths worldwide. In recent years, molecular and immunohistochemical techniques have rapidly developed, further inaugurating an era of personalized medicine for lung cancer. The rare subset of lung cancers accounts for approximately 10%, each displaying distinct clinical characteristics. Treatments for rare lung cancers are mainly based on evidence from common counterparts, which may lead to unsolid clinical benefits considering intertumoral heterogeneity. The increasing knowledge of molecular profiling of rare lung cancers has made targeting genetic alterations and immune checkpoints a powerful strategy. Additionally, cellular therapy has emerged as a promising way to target tumor cells. In this review, we first discuss the current status of targeted therapy and preclinical models for rare lung cancers, as well as provide mutational profiles by integrating the results of existing cohorts. Finally, we point out the challenges and future directions for developing targeted agents for rare lung cancer.

Autologous humanized PDX modeling for immuno-oncology recapitulates features of the human tumor microenvironment

BACKGROUND

Interactions between immune and tumor cells are critical to determining cancer progression and response. In addition, preclinical prediction of immune-related drug efficacy is limited by interspecies differences between human and mouse, as well as inter-person germline and somatic variation. To address these gaps, we developed an autologous system that models the tumor microenvironment (TME) from individual patients with solid tumors.

METHOD

With patient-derived bone marrow hematopoietic stem and progenitor cells (HSPCs), we engrafted a patient's hematopoietic system in MISTRG6 mice, followed by transfer of patient-derived xenograft (PDX) tissue, providing a fully genetically matched model to recapitulate the individual's TME. We used this system to prospectively study tumor-immune interactions in patients with solid tumor.

RESULTS

Autologous PDX mice generated innate and adaptive immune populations; these cells populated the TME; and tumors from autologously engrafted mice grew larger than tumors from non-engrafted littermate controls. Single-cell transcriptomics revealed a prominent vascular endothelial growth factor A (VEGFA) signature in TME myeloid cells, and inhibition of human VEGF-A abrogated enhanced growth.

CONCLUSIONS

Humanization of the interleukin 6 locus in MISTRG6 mice enhances HSPC engraftment, making it feasible to model tumor-immune interactions in an autologous manner from a bedside bone marrow aspirate. The TME from these autologous tumors display hallmarks of the human TME including innate and adaptive immune activation and provide a platform for preclinical drug testing.

Molecular Characterization and Landscape of Breast cancer Models from a multi-omics Perspective

Breast cancer is well-known to be a highly heterogenous disease. This facet of cancer makes finding a research model that mirrors the disparate intrinsic features challenging. With advances in multi-omics technologies, establishing parallels between the various models and human tumors is increasingly intricate. Here we review the various model systems and their relation to primary breast tumors using available omics data platforms. Among the research models reviewed here, breast cancer cell lines have the least resemblance to human tumors since they have accumulated many mutations and copy number alterations during their long use. Moreover, individual proteomic and metabolomic profiles do not overlap with the molecular landscape of breast cancer. Interestingly, omics analysis revealed that the initial subtype classification of some breast cancer cell lines was inappropriate. In cell lines the major subtypes are all well represented and share some features with primary tumors. In contrast, patient-derived xenografts (PDX) and patient-derived organoids (PDO) are superior in mirroring human breast cancers at many levels, making them suitable models for drug screening and molecular analysis. While patient derived organoids are spread across luminal, basal- and normal-like subtypes, the PDX samples were initially largely basal but other subtypes have been increasingly described. Murine models offer heterogenous tumor landscapes, inter and intra-model heterogeneity, and give rise to tumors of different phenotypes and histology. Murine models have a reduced mutational burden compared to human breast cancer but share some transcriptomic resemblance, and representation of many breast cancer subtypes can be found among the variety subtypes. To date, while mammospheres and three- dimensional cultures lack comprehensive omics data, these are excellent models for the study of stem cells, cell fate decision and differentiation, and have also been used for drug screening. Therefore, this review explores the molecular landscapes and characterization of breast cancer research models by comparing recent published multi-omics data and analysis.

Estrogen receptor alpha mutations regulate gene expression and cell growth in breast cancer through microRNAs

Estrogen receptor α (ER) mutations occur in up to 30% of metastatic ER-positive breast cancers. Recent data has shown that ER mutations impact the expression of thousands of genes not typically regulated by wildtype ER. While the majority of these altered genes can be explained by constant activity of mutant ER or genomic changes such as altered ER binding and chromatin accessibility, as much as 33% remain unexplained, indicating the potential for post-transcriptional effects. Here, we explored the role of microRNAs in mutant ER-driven gene regulation and identified several microRNAs that are dysregulated in ER mutant cells. These differentially regulated microRNAs target a significant portion of mutant-specific genes involved in key cellular processes. When the activity of microRNAs is altered using mimics or inhibitors, significant changes are observed in gene expression and cellular proliferation related to mutant ER. An in-depth evaluation of miR-301b led us to discover an important role for PRKD3 in the proliferation of ER mutant cells. Our findings show that microRNAs contribute to mutant ER gene regulation and cellular effects in breast cancer cells.

The future of patient-derived xenografts in prostate cancer research

Patient-derived xenografts (PDXs) are generated by engrafting human tumours into mice. Serially transplantable PDXs are used to study tumour biology and test therapeutics, linking the laboratory to the clinic. Although few prostate cancer PDXs are available in large repositories, over 330 prostate cancer PDXs have been established, spanning broad clinical stages, genotypes and phenotypes. Nevertheless, more PDXs are needed to reflect patient diversity, and to study new treatments and emerging mechanisms of resistance. We can maximize the use of PDXs by exchanging models and datasets, and by depositing PDXs into biorepositories, but we must address the impediments to accessing PDXs, such as institutional, ethical and legal agreements. Through collaboration, researchers will gain greater access to PDXs representing diverse features of prostate cancer.

Using cancer proteomics data to identify gene candidates for therapeutic targeting

Gene-level associations obtained from mass-spectrometry-based cancer proteomics datasets represent a resource for identifying gene candidates for functional studies. When recently surveying proteomic correlates of tumor grade across multiple cancer types, we identified specific protein kinases having a functional impact on uterine endometrial cancer cells. This previously published study provides just one template for utilizing public molecular datasets to discover potential novel therapeutic targets and approaches for cancer patients. Proteomic profiling data combined with corresponding multi-omics data on human tumors and cell lines can be analyzed in various ways to prioritize genes of interest for interrogating biology. Across hundreds of cancer cell lines, CRISPR loss of function and drug sensitivity scoring can be readily integrated with protein data to predict any gene's functional impact before bench experiments are carried out. Public data portals make cancer proteomics data more accessible to the research community. Drug discovery platforms can screen hundreds of millions of small molecule inhibitors for those that target a gene or pathway of interest. Here, we discuss some of the available public genomic and proteomic resources while considering approaches to how these could be leveraged for molecular biology insights or drug discovery. We also demonstrate the inhibitory effect of BAY1217389, a TTK inhibitor recently tested in a Phase I clinical trial for the treatment of solid tumors, on uterine cancer cell line viability.

Patient-derived xenograft models in cancer therapy: technologies and applications

Patient-derived xenograft (PDX) models, in which tumor tissues from patients are implanted into immunocompromised or humanized mice, have shown superiority in recapitulating the characteristics of cancer, such as the spatial structure of cancer and the intratumor heterogeneity of cancer. Moreover, PDX models retain the genomic features of patients across different stages, subtypes, and diversified treatment backgrounds. Optimized PDX engraftment procedures and modern technologies such as multi-omics and deep learning have enabled a more comprehensive depiction of the PDX molecular landscape and boosted the utilization of PDX models. These irreplaceable advantages make PDX models an ideal choice in cancer treatment studies, such as preclinical trials of novel drugs, validating novel drug combinations, screening drug-sensitive patients, and exploring drug resistance mechanisms. In this review, we gave an overview of the history of PDX models and the process of PDX model establishment. Subsequently, the review presents the strengths and weaknesses of PDX models and highlights the integration of novel technologies in PDX model research. Finally, we delineated the broad application of PDX models in chemotherapy, targeted therapy, immunotherapy, and other novel therapies.

Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials

Co-clinical trials are the concurrent or sequential evaluation of therapeutics in both patients clinically and patient-derived xenografts (PDX) pre-clinically, in a manner designed to match the pharmacokinetics and pharmacodynamics of the agent(s) used. The primary goal is to determine the degree to which PDX cohort responses recapitulate patient cohort responses at the phenotypic and molecular levels, such that pre-clinical and clinical trials can inform one another. A major issue is how to manage, integrate, and analyze the abundance of data generated across both spatial and temporal scales, as well as across species. To address this issue, we are developing MIRACCL (molecular and imaging response analysis of co-clinical trials), a web-based analytical tool. For prototyping, we simulated data for a co-clinical trial in "triple-negative" breast cancer (TNBC) by pairing pre- (T0) and on-treatment (T1) magnetic resonance imaging (MRI) from the I-SPY2 trial, as well as PDX-based T0 and T1 MRI. Baseline (T0) and on-treatment (T1) RNA expression data were also simulated for TNBC and PDX. Image features derived from both datasets were cross-referenced to omic data to evaluate MIRACCL functionality for correlating and displaying MRI-based changes in tumor size, vascularity, and cellularity with changes in mRNA expression as a function of treatment.